AFERSM-Net: Joint Network for Gesture Recognition and Location Classification

Abstract

With the widespread deployment of wireless communication systems and smart devices, gesture recognition and indoor location classification technologies based on WiFi wireless devices are increasingly used. Its technical principle is to identify human activities and locations by extracting gesture and location features from WiFi channel state information (CSI). However, the signal is susceptible to interference from the environment during CSI data acquisition to produce multipath effect noise, and the amplitude change with the change of location often affects the extraction and recognition of gesture features. To address these problems, Auxiliary Feature Extraction based Residual Shrinkage Multi-tasking Network (AFERSM-Net) is proposed for gesture recognition and position classification of one-dimensional multivariate time series. AFERSM-Net is a hybrid architecture that combines CNN for spatial feature extraction and LSTM networks for capturing temporal dependencies. Firstly, a reasonable threshold is set adaptively by the shrinkage module to dynamically identify and eliminate the transformed environmental noise. Secondly, the feature extraction module is used to focus on and extract location-independent gesture features to reduce the influence of location-independent features. Finally, the gesture features extracted by the feature extraction module are fused with the shared features of the residual shrinkage multi-tasking network as an aid. Its module fusion is mainly used to improve the accuracy of gesture recognition and solve the problem of insufficient model generalization ability. We evaluated this network on a dual-labeled gesture and location dataset, an the gesture recognition accuracy was 97.84% and the location classification accuracy was 98.92%, which outperformed other advanced network frameworks.